phi-4
Phi-4 is a 14B-parameter open-source language model from Microsoft designed for efficiency in memory-constrained and latency-sensitive environments. It excels at reasoning, mathematics, and code generation, trained on 9.8T tokens including synthetic datasets and filtered public sources. Released December 2024 under the MIT license, it is ungated and ready for immediate commercial and research use.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | microsoft |
| Parameters | 14.7B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 897.1k |
| Likes | 2.3k |
| Last updated | 2025-11-24 |
| Source | microsoft/phi-4 |
What phi-4 is
Dense decoder-only Transformer with 14.66B parameters, 16K token context length. Trained for 21 days on 1920 H100-80G GPUs using a blend of synthetic, public-domain, and acquired academic data. Post-training includes supervised fine-tuning and direct preference optimization for safety and instruction adherence. Benchmarked against MMLU, MATH, GPQA, HumanEval, and SimpleQA using OpenAI's SimpleEval framework.
Run phi-4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="microsoft/phi-4")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated 29–32 GB VRAM for full FP32 inference on a single GPU. Quantization (int8/int4) reduces to ~8–16 GB. Trained on 1920 H100-80G GPUs; production single-GPU inference plausible on A100 (40GB), H100, or RTX 4090 (24GB with optimizations). CPU inference possible but latency-sensitive; requires verification per deployment.
LoRA/QLoRA fine-tuning is plausible given 14B parameter size and chat-format training data. Model card does not explicitly detail fine-tuning guidelines or safeguards. Standard transformer-based fine-tuning approaches (Hugging Face Transformers, PEFT) should be compatible. Safety alignment was achieved via SFT + iterative DPO; consider similar techniques to preserve safety post-tuning.
When to avoid it — and what to weigh
- High-Volume Factual Knowledge Retrieval — SimpleQA score of 3.0 is significantly lower than GPT-4o (39.4) and peers. Not suitable for knowledge-heavy QA without retrieval augmentation (RAG).
- Multi-Language or Non-English Applications — Model card states English focus. Multilingual data is only ~8% of training set. Avoid for production use in non-English languages.
- High-Risk Downstream Applications Without Evaluation — Model card explicitly states that developers must evaluate and mitigate for accuracy, safety, and fairness for high-risk scenarios (healthcare, finance, legal). No guarantees provided.
- Real-Time Knowledge Cutoff Requirements — Static model trained on data with cutoff June 2024 and earlier. Not suitable for applications requiring current events or live information without external integration.
License & commercial use
Released under MIT license, a permissive OSI-approved license allowing unrestricted commercial use, modification, and redistribution with attribution. No restrictions on model weights or use case.
MIT license explicitly permits commercial use without restriction. Model is ungated (gated: false), allowing immediate download and deployment. No licensing review required for commercial applications. However, downstream use case evaluation and safety mitigation remain the developer's responsibility per model card guidance.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model underwent red-teaming by Microsoft's independent AI Red Team (AIRT) against jailbreaks, encoding attacks, multi-turn attacks, and adversarial suffixes in both average and adversarial user scenarios. Safety post-training used SFT and DPO with synthetic and curated datasets. No security vulnerabilities disclosed. Standard LLM considerations apply: prompt injection risks, potential for misuse in high-risk domains (medical, legal), and dependency on input validation in production systems. Model is deterministic; no claims on robustness to adversarial prompts.
Alternatives to consider
Phi-3 (14B, Microsoft)
Direct predecessor with similar efficiency profile but lower reasoning performance (MATH 44.6 vs. Phi-4 80.4). Consider if legacy system compatibility or stability is prioritized over capability.
Qwen 2.5 (14B Instruct, Alibaba)
Comparable size and reasoning (MATH 75.6), stronger DROP (85.5 vs 75.5), but weaker GPQA (42.9 vs 56.1). Alternative for balanced reasoning and comprehension without Microsoft dependency.
Llama 3.3 (70B Instruct, Meta)
Larger, stronger across most benchmarks (MMLU 86.3, MATH 77), but 5× parameter count. Choose if compute is available and maximum capability is required; less suitable for edge/latency-critical use.
Ship phi-4 with senior software developers
Phi-4 is MIT-licensed and immediately available for commercial use. Evaluate it in your environment using vLLM, TGI, or Ollama. For guidance on private deployment, RAG integration, or fine-tuning, contact our AI engineering team.
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phi-4 FAQ
Can I use Phi-4 commercially without additional licensing?
What are the minimum GPU requirements for inference?
Is Phi-4 suitable for production chatbots?
What's the knowledge cutoff, and can I fine-tune it on newer data?
Software development & web development with DEV.co
Need help beyond evaluating phi-4? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy Phi-4?
Phi-4 is MIT-licensed and immediately available for commercial use. Evaluate it in your environment using vLLM, TGI, or Ollama. For guidance on private deployment, RAG integration, or fine-tuning, contact our AI engineering team.